Project Summary and Abstract Falls are the leading cause of death due to injury. This simple motion is so common that 30% of community dwelling older adults, and 50% of long term care facilities will experience a fall in the coming year. The risk of falling substantially increases for those having Alzheimer?s disease and related dementias, and those with Parkinson?s disease. The financial burden is significant with fall-related costs being $32 billion in 2016, with costs expected to rise to $44 billion by 2020. Care-giving institutions, who are often liable for the well-being of their patients, bear a substantial portion of the cost. A fall can cost $12,817 per case for long term care facilities and can cost $13,000 per case for hospitals. Commercially available fall detection systems operate wearable pendant-based devices that a patient presses after experiencing a fall. Newer generations of these systems also incorporate accelerometers that are reportedly able to detect falls. These systems are user- dependent, meaning that a patient must be wearing the pendant for it to work which older adults, particularly those with cognitive impairments, often do not. Furthermore, the patient has to be cognizant to press the button to call for aid if the pendant does not activate during a fall. This is unlikely to occur as even when people are not cognitively impaired they will only activate the system 20% of the time. There is a clear need for an automated, user-independent fall detection system. Better yet would be a system that can detect non-injurious falls or changes in gait, both of which are predictors of oncoming injurious falls. ASSET, in partnership with the University of South Carolina, have developed a patent-pending, floor vibration fall detection and gait analysis prototype system that can detect non-injurious falls and collect gait information whilst being user-independent. The innovation has the ability to firmly place control of liability back into the hands of care-giving institutions much like what a fire alarm does for property damage from fires, and potentially saving ~$2.2 billion in fall-related costs with just 5% market adoption. During Phase I, the system will be tested and algorithms refined in a deployment of eight systems to dwellings whose residents are at high risk of falling, and compare the gait analysis capabilities to that of the GAITRite Mat and ADPM system with an expected n of 58. Focus groups of stakeholders in the use of the innovation will also be conducted. The work in Phase I are expected to result in a refined alpha version of the fall detection and gait analysis technology, with goals of >90% accuracy in fall detection and comparable gait information results to that of existing standards. Strong results in Phase I will warrant further development in a larger deployment of the technology in Phase II to validate and finalize the technology in preparation for commercialization.